Identification of Motor Unit Twitch Properties in the Intact Human In Vivo

Restoring natural motor function in neurologically injured individuals is challenging, largely due to the lack of personalization in current neurorehabilitation technologies. Signal-driven neuro-musculoskeletal models may offer a novel paradigm for devising novel closed-loop rehabilitation strategies according to an individual's physiology. However, current modelling techniques are constrained to bipolar electromyography (EMG), thereby lacking the resolution necessary to extract the activity of individual motor units (MUs) in vivo. In this work, we decoded MU spike trains from high-density (HD)-EMG to obtain relevant neural properties across multiple isometric plantar-dorsiflexion tasks. Then, we sampled MU statistical distributions and used them to reproduce MU specific activation profiles. Results showed bimodal distributions which may correspond to slow and fast MU populations. The estimated activation profiles showed a high degree of similarity to the reference torque (R2>0.8) across the recorded muscles. This suggests that the estimation of MU twitch properties is a crucial step for the translation of neural information into muscle force.Clinical Relevance- This work has multiple implications for understanding the underlying mechanism of motor impairment and for developing closed-loop strategies for modulating alpha motor circuitries in neurologically injured individuals

Adaptation Strategies for Personalized Gait Neuroprosthetics

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.Peer ReviewedPostprint (published version

Person-Specific Biophysical Modeling of Alpha-Motoneuron Pools Driven by in vivo Decoded Neural Synaptic Input

Interfacing with alpha-motoneurons (MNs) is key to understand and control motor impairment and neurorehabilitation technologies. Depending on the neurophysiological condition of each individual, MN pools exhibit distinct neuro-anatomical properties and firing behaviors. Hence, the ability to assess subject-specific characteristics of MN pools is essential for unravelling the neural mechanisms and adaptations underlying motor control, both in healthy and impaired individuals. However, measuring in vivo the properties of complete human MN pools remains an open challenge. Therefore, this work proposes a novel approach based on decoding neural discharges from human MNs in vivo for driving the metaheuristic optimization of biophysically realistic MN models. First, we show that this framework provides subject-specific estimates of MN pool properties from the tibialis anterior muscle on five healthy individuals. Second, we propose a methodology to create complete pools of in silico MNs for each subject. Lastly, we show that neural-data driven complete in silico MN pools reproduce in vivo MN firing characteristics and muscle activation profiles during force-tracking tasks involving isometric ankle dorsi-flexion, at different levels of amplitude. This approach can open new avenues for understanding human neuro-mechanics and, particularly, MN pool dynamics, in a person-specific way. Thereby enabling the development of personalized neurorehabilitation and motor restoring technologies

On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals.

We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine learning techniques. The model training is based on biosignals acquired in a randomized controlled trial. Wearable sensors were used to collect electrocardiogram, electrodermal activity, and respiration from spider-fearful individuals. We designed and applied ten approaches for data labeling considering individual biosignals as well as subjective ratings. Performance results revealed a selection of trained models adapted for two-level (low and high) and three-level (low, medium and high) classification of anxiety using a minimal set of six features. We obtained a remarkable accuracy of 89.8% for the two-level classification and of 74.4% for the three-level classification using a short time window length of ten seconds when applying the approach that uses subjective ratings for data labeling. Bagged Trees proved to be the most suitable classifier type among the classification models studied. The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia

Interfacing With Alpha Motor Neurons in Spinal Cord Injury Patients Receiving Trans-spinal Electrical Stimulation

Trans-spinal direct current stimulation (tsDCS) provides a non-invasive, clinically viable approach to potentially restore physiological neuromuscular function after neurological impairment, e.g., spinal cord injury (SCI). Use of tsDCS has been hampered by the inability of delivering stimulation patterns based on the activity of neural targets responsible to motor function, i.e., α-motor neurons (α-MNs). State of the art modeling and experimental techniques do not provide information about how individual α-MNs respond to electrical fields. This is a major element hindering the development of neuro-modulative technologies highly tailored to an individual patient. For the first time, we propose the use of a signal-based approach to infer tsDCS effects on large α-MNs pools in four incomplete SCI individuals. We employ leg muscles spatial sampling and deconvolution of high-density fiber electrical activity to decode accurate α-MNs discharges across multiple lumbosacral segments during isometric plantar flexion sub-maximal contractions. This is done before, immediately after and 30 min after sub-threshold cathodal stimulation. We deliver sham tsDCS as a control measure. First, we propose a new algorithm for removing compromised information from decomposed α-MNs spike trains, thereby enabling robust decomposition and frequency-domain analysis. Second, we propose the analysis of α-MNs spike trains coherence (i.e., frequency-domain) as an indicator of spinal response to tsDCS. Results showed that α-MNs spike trains coherence analysis sensibly varied across stimulation phases. Coherence analyses results suggested that the common synaptic input to α-MNs pools decreased immediately after cathodal tsDCS with a persistent effect after 30 min. Our proposed non-invasive decoding of individual α-MNs behavior may open up new avenues for the design of real-time closed-loop control applications including both transcutaneous and epidural spinal electrical stimulation where stimulation parameters are adjusted on-the-fly

On-line anxiety level detection from biosignals: Machine learning based on a randomized controlled trial with spider-fearful individuals

We present performance results concerning the validation for anxiety level detection based on trained mathematical models using supervised machine learning techniques. The model training is based on biosignals acquired in a randomized controlled trial. Wearable sensors were used to collect electrocardiogram, electrodermal activity, and respiration from spiderfearful individuals. We designed and applied ten approaches for data labeling considering individual biosignals as well as subjective ratings. Performance results revealed a selection of trained models adapted for two-level (low and high) and three-level (low, medium and high) classification of anxiety using a minimal set of six features. We obtained a remarkable accuracy of 89.8% for the two-level classification and of 74.4% for the three-level classification using a short time window length of ten seconds when applying the approach that uses subjective ratings for data labeling. Bagged Trees proved to be the most suitable classifier type among the classification models studied. The trained models will have a practical impact on the feasibility study of an augmented reality exposure therapy based on a therapeutic game for the treatment of arachnophobia